Safe Online Adaptive Refinement (SOAR-Flow), a framework for safe and stable online fine-tuning of offline-initialized Flow-based driving policies, introduces Gaussian-return-aware value calibration based on calibrated Q-learning (Cal-QL), to support stable policy updates.
Ze-Qiao Li, Yi-Jing Wang, Hao-Yu Wang et al.· 0 citations
Distributional critics model the full return distribution, providing richer signals for exploration and risk-sensitive control. Recent work has shown that flow-based critics can model these distributions flexibly and provide effective supervision across flow time for temporal-difference learning. However, unlike standa...
Ze-Qiao Li, Yi-Jing Wang, Hao-Yu Wang et al.· 0 citations
This paper investigates model predictive control (MPC) for switched systems subject to control and state constraints. A variable-horizon switched MPC approach is proposed. By steering the system state into a well-designed switching feasible set, the proposed method structurally decouples the dwell-time conditions from...
DeGuNet is presented, an ultra-compact and plug-and-play image backbone explicitly designed for depth-guided representation learning that effectively aligns multi-view images with unstructured LiDAR depth while strictly preventing invalid-region contamination.
